It is generally agreed that many proteins are structurally dynamic; sampling many conformations while in solution and also adopting new conformations upon complexation with a ligand. Many of these flexible enzymes are of biological interest, and hindering their function via binding of competitive inhibitors would open up valuable therapeutic avenues. Unfortunately due to the conformation-dependent nature of ligand binding, the act of discovering a new small molecule that will bind these particular proteins is analogous to aiming at a moving target. The following work focuses on one particular enzyme, glutamate racemase. Glutamate racemase is an essential and non-redundant enzyme in all species of bacteria, and inhibition of this enzyme results in cell wall degradation, followed by imminent cell death. Inhibitors of glutamate racemase could act as novel antibiotics against a target to which there are no current antibiotics, and thus no known resistance. My studies focus on three interdependent phenomenon related to enzymes: protein dynamics, ligand binding, and catalysis. Three main thrusts of my research lay at the intersection of these physical phenomenon. First and foremost, the Spies lab is interested in structure-based computer-aided drug discovery, and the discovery of glutamate racemase inhibitors is a project located at the intersection of ligand binding and catalysis, where small molecules inhibit the catalytic process. My second project builds on this by adding a deeper understanding of noncompetitive GR inhibitors and allostery in general. This entails exploring the relationship between protein dynamics and catalysis. Finally, my third project involves more fundamental biochemistry in that we closely examine facets of molecular recognition such as conformational changes induced by ligand binding, and the role of interstitial water. The results of the second two projects then feed back into our in silico methods in order to improve our capacity to predict small molecule binders of glutamate racemase. Much of the knowledge detailed here can be applied to similar proteins of alternate classes, thus improving structure-based computer-aided drug discovery against many flexible proteins.